Model-Based Graphics Recognition
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
An interactive system for recognizing hand drawn UML diagrams
CASCON '00 Proceedings of the 2000 conference of the Centre for Advanced Studies on Collaborative research
Using grammars for pattern recognition in images: A systematic review
ACM Computing Surveys (CSUR)
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We present a probabilistic framework for document analysis and recognition and illustrate it on the problem of musical score recognition. Our system uses an explicit descriptive model of the document class to find the most likely interpretation of a scanned document image. In contrast to the traditional pipeline architecture, we carry out all stages of the analysis with a single inference engine, allowing for an end-to-end propagation of the uncertainty. The global modeling structure is similar to a stochastic attribute grammar, and local parameters are estimated using hidden Markov models.